LAB11

Here is my lab output here

Learning Goals

• Read in and process the COVID dataset from the New York Times GitHub repository
• Create interactive graphs of different types using plot_ly() and ggplotly() functions
• Customize the hoverinfo and other plot features
• Create a Choropleth map using plot_geo()

Lab Description

We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.

The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this

Step I. Reading and processing the New York Times (NYT) state-level COVID-19 data

Read in the data

## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data

## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data

### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))

### FINISH THE CODE HERE ###
# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

### FINISH THE CODE HERE
cv_states <- merge(cv_states, state_pops, by = "state")

Look at the data

• Inspect the dimensions, head, and tail of the data
• Inspect the structure of each variables. Are they in the correct format?

knitr::kable(dim(cv_states))

x
32250
9

knitr::kable(head(cv_states))

state	date	fips	cases	deaths	geo_id	population	pop_density	abb
Alabama	2020-06-10	1	21989	744	1	4887871	96.50939	AL
Alabama	2021-10-31	1	832047	15573	1	4887871	96.50939	AL
Alabama	2021-05-26	1	542831	11138	1	4887871	96.50939	AL
Alabama	2020-04-19	1	4903	160	1	4887871	96.50939	AL
Alabama	2021-07-07	1	552911	11387	1	4887871	96.50939	AL
Alabama	2020-06-21	1	30021	839	1	4887871	96.50939	AL

knitr::kable(tail(cv_states))

	state	date	fips	cases	deaths	geo_id	population	pop_density	abb
32245	Wyoming	2020-12-01	56	33805	230	56	577737	5.950611	WY
32246	Wyoming	2021-08-15	56	68272	793	56	577737	5.950611	WY
32247	Wyoming	2021-03-16	56	55352	693	56	577737	5.950611	WY
32248	Wyoming	2021-04-12	56	56988	701	56	577737	5.950611	WY
32249	Wyoming	2021-04-01	56	56389	700	56	577737	5.950611	WY
32250	Wyoming	2020-11-30	56	33305	215	56	577737	5.950611	WY

knitr::kable(str(cv_states))

## 'data.frame':    32250 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : IDate, format: "2020-06-10" "2021-10-31" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  21989 832047 542831 4903 552911 30021 117242 809485 134417 547135 ...
##  $ deaths     : int  744 15573 11138 160 11387 839 2037 14869 2285 11252 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

|| || || ||

Format the data

• Make date into a date variable
• Make state into a factor variable
• Order the data first by state, second by date
• Confirm the variables are now correctly formatted
• Inspect the range values for each variable. What is the date range? The range of cases and deaths?

# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

### FINISH THE CODE HERE 
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]

# Confirm the variables are now correctly formatted
knitr::kable(str(cv_states))

## 'data.frame':    32250 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...

|| || || ||

knitr::kable(head(cv_states))

	state	date	fips	cases	deaths	geo_id	population	pop_density	abb
215	Alabama	2020-03-13	1	6	0	1	4887871	96.50939	AL
428	Alabama	2020-03-14	1	12	0	1	4887871	96.50939	AL
69	Alabama	2020-03-15	1	23	0	1	4887871	96.50939	AL
496	Alabama	2020-03-16	1	29	0	1	4887871	96.50939	AL
341	Alabama	2020-03-17	1	39	0	1	4887871	96.50939	AL
53	Alabama	2020-03-18	1	51	0	1	4887871	96.50939	AL

knitr::kable(tail(cv_states))

	state	date	fips	cases	deaths	geo_id	population	pop_density	abb
31735	Wyoming	2021-11-06	56	105318	1243	56	577737	5.950611	WY
31791	Wyoming	2021-11-07	56	105318	1243	56	577737	5.950611	WY
31967	Wyoming	2021-11-08	56	105990	1243	56	577737	5.950611	WY
31942	Wyoming	2021-11-09	56	106287	1298	56	577737	5.950611	WY
31961	Wyoming	2021-11-10	56	106698	1298	56	577737	5.950611	WY
32016	Wyoming	2021-11-11	56	106698	1298	56	577737	5.950611	WY

# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
knitr::kable(head(cv_states))

	state	date	fips	cases	deaths	geo_id	population	pop_density	abb
215	Alabama	2020-03-13	1	6	0	1	4887871	96.50939	AL
428	Alabama	2020-03-14	1	12	0	1	4887871	96.50939	AL
69	Alabama	2020-03-15	1	23	0	1	4887871	96.50939	AL
496	Alabama	2020-03-16	1	29	0	1	4887871	96.50939	AL
341	Alabama	2020-03-17	1	39	0	1	4887871	96.50939	AL
53	Alabama	2020-03-18	1	51	0	1	4887871	96.50939	AL

knitr::kable(summary(cv_states))

	state	date	fips	cases	deaths	geo_id	population	pop_density	abb
	Washington : 661	Min. :2020-01-21	Min. : 1.00	Min. : 1	Min. : 0	Min. : 1.00	Min. : 577737	Min. : 1.292	WA : 661
	Illinois : 658	1st Qu.:2020-08-03	1st Qu.:16.00	1st Qu.: 31774	1st Qu.: 621	1st Qu.:16.00	1st Qu.: 1805832	1st Qu.: 43.659	IL : 658
	California : 657	Median :2021-01-05	Median :29.00	Median : 146459	Median : 2658	Median :29.00	Median : 4468402	Median : 107.860	CA : 657
	Arizona : 656	Mean :2021-01-05	Mean :29.78	Mean : 386609	Mean : 7155	Mean :29.78	Mean : 6433897	Mean : 422.513	AZ : 656
	Massachusetts: 650	3rd Qu.:2021-06-09	3rd Qu.:44.00	3rd Qu.: 481786	3rd Qu.: 8432	3rd Qu.:44.00	3rd Qu.: 7535591	3rd Qu.: 229.511	MA : 650
	Wisconsin : 646	Max. :2021-11-11	Max. :72.00	Max. :4993930	Max. :73132	Max. :72.00	Max. :39557045	Max. :11490.120	WI : 646
	(Other) :28322	NA	NA	NA	NA	NA	NA	NA’s :609	(Other):28322

knitr::kable(min(cv_states$date))

x
2020-01-21

knitr::kable(max(cv_states$date))

x
2021-11-11

Add new_cases and new_deaths and correct outliers

• Add variables for new cases, new_cases, and new deaths, new_deaths:
• Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2
• Filter to dates after June 1, 2021
• Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
• Correct outliers: Set negative values for new_cases or new_deaths to 0
• Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths
• Get the rolling average of new cases and new deaths to smooth over time
• Inspect data again interactively

# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]
  
  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]
  
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1]
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }
  
  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}

# Focus on recent dates
cv_states <- cv_states %>% dplyr::filter(date >= "2021-06-01")

### FINISH THE CODE HERE
# Inspect outliers in new_cases using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + 
  geom_line() + 
  geom_point(size = .5, alpha = 0.5)
ggplotly(p1)

p1<-NULL # to clear from workspace

p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + 
  geom_line() + 
  geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

p2<-NULL # to clear from workspace

# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0

# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])

  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]

  ### FINISH CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)

# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

#p2=NULL

Add additional variables

Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:

• per100k = cases per 100,000 population
• newper100k= new cases per 100,000
• deathsper100k = deaths per 100,000
• newdeathsper100k = new deaths per 100,000

Add a “naive CFR” variable representing deaths / cases on each date for each state

Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))

# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))

# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))

Explore scatterplots using plot_ly()

• Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
• Color points by state and size points by state population
• Use hover to identify any outliers.
• Remove those outliers and replot.
• Choose one plot. For this plot:
• Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
• Add layout information to title the chart and the axes
• Enable hovermode = “compare”

### FINISH CODE HERE

# pop_density vs. cases
cv_states_today %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# filter out "District of Columbia"
cv_states_today_filter <- cv_states_today %>% filter(state!="District of Columbia")

# pop_density vs. cases after filtering
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# pop_density vs. deathsper100k
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# Adding hoverinfo
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , 
                         paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>")) %>%
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
                  yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
         hovermode = "compare")

Explore scatterplot trend interactively using ggplotly() and geom_smooth()

• For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
• Explore the pattern between and using geom_smooth()
• Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?

### FINISH CODE HERE
p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population)) + 
  geom_point() + 
  geom_smooth()
ggplotly(p)

Multiple line chart

• Create a line chart of the naive_CFR for all states over time using plot_ly()
• Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?
• Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()
• Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?

### FINISH CODE HERE
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")

### FINISH CODE HERE
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>% 
  filter(state=="Florida") %>% 
  plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% 
  add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines") 

Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks

### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% filter(date>as.Date("2020-04-01"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2020-04-01"), as.Date("2020-10-01"), by="2 weeks")
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

Map

• Create a map to visualize the naive_CFR by state on October 15, 2021
• Compare with a map visualizing the naive_CFR by state on most recent date
• Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this)
• Describe the difference in the pattern of the CFR.

### For specified date

pick.date = "2021-10-15"

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>% filter(date==pick.date) %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_pick.date <- fig

#############
### Map for today's date

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>%  select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig


### Plot together 
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)